The Future of Clinical Research
Intelligence that accelerates clinical trials
Axiom AI Clinical Monitor - built for sponsor clinical operations and central monitoring teams
From 8-week site visits to continuous oversight. An AI agent that reads source data continuously, reconciles against the EDC, and produces monitoring visit reports - citation-grounded and ready for CRA sign-off.
The Problem
Up to a quarter of your trial budget confirms data that is already correct
Up to 25%
Of trial budget consumed by on-site SDV.
Andersen et al., Br J Clin Pharmacol, 2023.
99.5%
Of data fields already accurate - error rate just 0.45%.
Andersen et al., 2015; 3M+ fields, 3 Phase 3 RCTs.
46%
Of CRA on-site time spent on SDV vs. high-value work.
Applied Clinical Trials field survey.
4–8 wk
Blind window between visits - findings lag weeks behind standard industry monitoring cadence.
The Dollar Impact
A typical Phase 3 study spends roughly $2M on monitoring labor alone - about 9–14% of trial operating cost. Risk-based approaches recover an estimated 30–40%: on the order of $500K–$1M per study.
Sertkaya et al., HHS/ASPE; industry RBM benchmarks.
CRAs visit sites every 4–8 weeks to reconcile EHR notes, labs, and imaging against the EDC line by line. Almost everything they check is already right. The findings that matter - missed AEs, protocol deviations, eligibility errors - can go undetected between visits. ICH E6(R3) now mandates risk-based, technology-enabled monitoring.
How Axiom works
An AI agent that reads source data continuously and produces monitoring reports
Protocol & CRF ingestion
Structured schema from protocol and CRF - human-verified once
Axiom reads the protocol and CRF spec once. An LLM extracts every data point, visit window, and prohibited medication into a structured schema. Your team verifies it; the agent uses it from then on.
Protocol & CRF
Verified extraction → agent-ready monitoring rules
Continuous source data reading
FHIR R4 to the site EHR - reconcile as data arrives
The agent connects to the site EHR via FHIR R4 and reads new notes, labs, and imaging as they arrive. For each CRF field, it locates the source evidence and reconciles against the EDC - flagging discrepancies, missed AEs, and deviations.
Source read
Continuous reconciliation vs. EDC with evidence links
MVR generation & CRA sign-off
Sponsor-formatted reports - TMF-ready, Part 11 audit-trailed
Axiom produces sponsor-formatted Monitoring Visit Reports ready for human CRA review and signature. Every finding cites the source line. CRAs sign; they no longer write from scratch.
MVR output
CRA review and signature on citation-backed findings
Why now
Regulation, models, and interoperability aligned
ICH E6(R3)
Risk-based, technology-enabled monitoring is now the expected standard (2023).
Long-context LLMs
A full patient chart can be ingested in one pass with structured extraction and citation grounding.
FHIR R4 maturity
Site-side EHR access is operationally viable, making continuous remote SDV feasible for the first time.
Proof Point
In the I-SPY COVID platform trial, retrospective 100% SDV across 10,101 eCRFs found zero errors that changed any result or conclusion. The trial used systematic data capture and centralized monitoring by design - not on-site verification.
Abbasi et al., Nature Communications Medicine, 2025. NCT04488081.
Built for your team
CRAs, data integrity, and sponsor oversight
For your CRAs
Augmented, not replaced
CRAs focus on site relationships, PI engagement, and safety judgment - the work SDV displaces.
For your data
Fully cited
Every finding links to a source document line. Hallucination rate measured and published.
For your oversight
Sponsor-owned
Full audit trail, full IP retention. Mandate in CRO work orders like Rave or Vault.
See continuous, citation-grounded monitoring in your workflow
Request a conversation about piloting Axiom AI Clinical Monitor with your sponsor clinical operations or central monitoring team - FHIR-connected source read, EDC reconciliation, and MVR-ready outputs.
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Ready for continuous clinical monitoring?
Tell us about your study and monitoring model - we'll follow up on how Clinical Monitor fits sponsor clinical operations and central monitoring.